In current image interpretation practice, such as diagnostic radiology, a specialist trained in interpreting images and recognizing abnormalities may look at an image or an image sequence on an image display and report any visual findings by dictating or typing the findings into a report template. The dictating or typing usually includes a narration of the finding, a description about the location of the visual phenomena, abnormality, or region of interest within the images being reported on. The recipient of the report is often left to further analyze the contents of the narrative text report without having easy access to the underlying image. More particularly, in current reporting practice, there is no link between the specific location in the image and the finding associated with the visual phenomena, abnormality, or region of interest, in the image. A specialist also may have to compare a current image with an image and report previously done. This leaves the interpreter to refer back and forth between the image and the report.
Computer-aided detection (CAD) systems are known in the art and are usually confined to detecting and classifying conspicuous structures in the image data. Computer-aided diagnosis (CAD) systems are used in mammography to highlight micro calcification clusters and hyperdense structures in the soft tissue. Computer-aided simple triage (CAST) is another type of CAD, which performs a fully automatic initial interpretation and triage of studies into some meaningful categories (e.g. negative and positive). Unfortunately, these prior art systems are limited to describing the location of the visual phenomena within the image file. By manner of illustration, the coordinate system provided by the CAD system cannot be used to guide a biopsy needle because it fails to identify the relative position within the organ or sample structure.
While such inconveniences may pose a seemingly insignificant risk of error, a typical specialist must interpret a substantial amount of such images in short periods of time, which further compounds the specialist's fatigue and vulnerability to oversights. This is especially critical when the images to be interpreted are medical images of patients with their health being at risk.
General articulation and narration of an image interpretation may be facilitated with reference to structured reporting templates or knowledge representations. One example of a knowledge representation in the form of a semantic network is the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT), which is a systematically organized and computer processable collection of medical terminology covering most areas of clinical information, such as diseases, findings, procedures, microorganisms, pharmaceuticals, and the like. SNOMED-CT provides a consistent way to index, store, retrieve, and aggregate clinical data across various specialties and sites of care. SNOMED-CT also helps in organizing the content of medical records, and in reducing the inconsistencies in the way data is captured, communicated, encoded, and used for clinical care of patients and research.
Another example is the Breast Imaging-Reporting and Data System (BI-RADS), which is a quality assurance tool originally designed for use with mammography. Yet another example is RadLex, a lexicon for uniform indexing and retrieval of radiology information resources, which currently includes more than 30,000 terms. Applications include radiology decision support, reporting tools and search applications for radiology research and education. Reporting templates developed by the Radiological Society of North America (RSNA) Reporting Committee use RadLex terms in their content. Reports using RadLex terms are clearer and more consistent, reducing the potential for error and confusion. RadLex includes other lexicons and semantic networks, such as SNOMED-CT, BI-RADS, as well as any other system or combination of systems developed to help structure and standardize reporting. Richer forms of semantic networks in terms of knowledge representation are ontologies. Ontologies are encoded using ontology languages and commonly include the following components: instances (the basic or “ground level” objects), classes (sets, collections, concepts, classes in programming, types of objects, or kinds of things), attributes (aspects, properties, features, characteristics, or parameters that objects), relations (ways in which classes and individuals can be related to one another), function terms (complex structures formed from certain relations that can be used in place of an individual term in a statement), restrictions (formally stated descriptions of what must be true in order for some assertion to be accepted as input), rules (statements in the form of an if-then sentence that describe the logical inferences that can be drawn from an assertion in a particular form, axioms (assertions, including rules, in a logical form that together comprise the overall theory that the ontology describes in its domain of application), and events (the changing of attributes or relations).
Currently existing image reporting mechanisms do not take full advantage of knowledge representations to assist interpretation while automating reporting. In particular, currently existing systems are not fully integrated with knowledge representations to provide seamless and effortless reference to knowledge representations during articulation of findings. Additionally, in order for such a knowledge representation interface to be effective, there must be a brokering service between the various forms of standards and knowledge representations that constantly evolve. While there is a general lack of such brokering service between the entities of most domains, there is an even greater deficiency in the available means to promote common agreements between terminologies, especially in image reporting applications. Furthermore, due to the lack of more streamlined agreements between knowledge representations in image reporting, currently existing systems also lack means for automatically tracking the development of specific and related cases for inconsistencies or errors so that the knowledge representations may be updated to provide more accurate information in subsequent cases. Such tracking means provide the basis for a probability model for knowledge representations.
In light of the foregoing, there is a need for an improved system and method for generating and managing image reports. There is also a need to automate several of the intermediary steps involved with image reporting and recalling image reports currently existing today. More specifically, there is a need to intertwine automated computer aided image mapping, recognition and reconstruction techniques with automated image reporting techniques. Furthermore, there is a need to integrate image reporting schemes with knowledge representation databases and to provide a means for tracking subsequent and related cases.